TY - GEN
T1 - Exploring EEG Features for Differentiating Between Secure and Insecure Attachment Styles
AU - Zuckerman, Inon
AU - Mizrahi, Dor
AU - Laufer, Ilan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Attachment theory is a psychological framework that explains the nature and development of human attachment relationships. Studies have also found that our attachment style is prominent in how we form and behave in significant romantic relationships in our adult lives. Today, the current practice of measuring one’s attachment is either via psychotherapy-based treatment or self-report attachment questionnaires. However, self-report measures rely on an individual's self-awareness and willingness to accurately report their thoughts and feelings, which can be influenced by social desirability bias or other factors. Today, with the availability of various EEG-based features that show some differences in activation patterns, we explore whether the attachment styles can be differentiated, via brain waves activations, when participants played a simple unrelated task, the flanker task. Our results show that successful classification can be attained using frequency, temporal, or complexity-based features. These results provide a first example of the ability to classify the primary attachment based on EEG features instead of self-report questionnaires.
AB - Attachment theory is a psychological framework that explains the nature and development of human attachment relationships. Studies have also found that our attachment style is prominent in how we form and behave in significant romantic relationships in our adult lives. Today, the current practice of measuring one’s attachment is either via psychotherapy-based treatment or self-report attachment questionnaires. However, self-report measures rely on an individual's self-awareness and willingness to accurately report their thoughts and feelings, which can be influenced by social desirability bias or other factors. Today, with the availability of various EEG-based features that show some differences in activation patterns, we explore whether the attachment styles can be differentiated, via brain waves activations, when participants played a simple unrelated task, the flanker task. Our results show that successful classification can be attained using frequency, temporal, or complexity-based features. These results provide a first example of the ability to classify the primary attachment based on EEG features instead of self-report questionnaires.
KW - Attachment theory
KW - EEG
KW - Lempel-Ziv complexity
KW - Theta to Beta Ratio (TBR)
UR - http://www.scopus.com/inward/record.url?scp=85186739894&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47718-8_29
DO - 10.1007/978-3-031-47718-8_29
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AN - SCOPUS:85186739894
SN - 9783031477171
T3 - Lecture Notes in Networks and Systems
SP - 436
EP - 448
BT - Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 4
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2023
Y2 - 7 September 2023 through 8 September 2023
ER -